Intrinsically disordered regions (IDRs) play essential roles in cellular signaling and regulation, primarily mediating interactions through short peptide motifs. Linear Interacting Peptides (LIPs) are a recently defined class of bindingassociated IDRs that undergo disorder-to-order transitions upon binding. LIPs encompass several well-characterized subclasses, including molecular recognition features (MoRFs) and short linear motifs (SLiMs). However, many current predictors focus exclusively on MoRFs prediction and exhibit limited effectiveness in identifying the broader class of LIPs. To address this limitation, we propose LipPredictor, the first deep learning framework specifically designed for LIP prediction. The model incorporates protein language model embeddings as input to a shared feature extraction module, which is composed of convolutional neural networks and a multi-head attention layer. It further adopts a dual-branch architecture that couples residue-level classification with auxiliary multi-scale anchor detection, enabling accurate identification of LIPs across diverse segment lengths through joint training. These results demonstrate its robustness and effectiveness in predicting LIPs. The source code can be obtained from https://github.com/Chenxi-Xia/LipPredictor.

AI4Biopharmaceutical is an important field of interest in both academia and industry. Due to the characteristics of biopharmaceutical industry data and the hardware limitation of real factories, biopharmaceutical indicators prediction models need to balance computational complexity and prediction effectiveness. We propose a low computational complexity hybrid model (LCCH) combing the biopharmaceutical mechanism with artificial intelligence for biopharmaceutical indicators prediction. We use the Doolittle method to reduce the computational complexity of the online decomposition phase to O(1), while employing the half-router attention incorporating biopharmaceutical mechanism to significantly reduce the computational complexity of the prediction phase. The model achieves the state-of-the-art results for the prediction of five biopharmaceutical indicators: amino nitrogen, reducing sugar, total sugar, bacterial concentration,and viscosity in the erythromycin pharmaceutical scenario. Compared to the other outstanding baseline models from the last three years, LCCH demonstrates the superiority and potential of the hybrid model for biopharmaceutical industrial applications.

To alleviate the security concerns caused by the openness of the edge computing network and meet the time-sensitive requirements of the edge devices' collaborative tasks, an effective trust evaluation mechanism is needed urgently to resist multi-attacks from various malicious devices. In this work, a light- weight trust mechanism based on multi-source feedback is proposed for edge computing. First, we design a light-weight data-processing algorithm executed in edge brokers and edge devices, which could reduce the data transmission pressure in communication networks effectively and work efficiently in large-scale edge networks. Then, a comprehensive evaluation method is designed for edge brokers based on the Dempster Shafer theory and multi-source feedback mechanism, which makes our mechanism more reliable and pluralistic when resisting various multi-attacks at the same time. At last, we originally develop a neural network in the centralized cloud to update edge brokers' hyper-parameters and weights of the key factors by auditing trust evaluation results uploaded from the edge network according to deep Q-learning algorithm, which are usually weighted manually and subjectively in traditional schemes. The experimental results show the proposed trust mechanism outperforms existing methods in reliability and calculation efficiency when resisting various malicious attacks.

With the development of the edge computing and cloud computing technology, the cloud-edge collaboration framework is proposed as a new effective computing architecture and applied in many fields. However, due to the openness of the edge networks, the security of cloud-edge framework is an unavoidable problem and most recent trust mechanism could not resist mixed malicious attacks at the same time. In this work, a light-weight and reliable trust mechanism based on the improved LightGBM algorithm is originally proposed to evaluate the credibility of edge devices. First, we design a light-weight trust mechanism for edge devices to process raw interaction data and extract trust features, which reduces the amount of data transmission and the pressure on the communication networks. In addition, an evaluation algorithm based on the entropy weight method (EWM) and punishment factors is designed for edge brokers to distinguish the malicious devices from the normal ones, which performs great against mixed malicious attacks. At last, we propose an improved LightGBM algorithm developed in the centralized cloud to learn other researchers' evaluation methods and check the evaluation uploaded from edge brokers, which could make the punishment factors of edge networks weighted adaptively with the change of edge networks. The experimental results show the proposed trust mechanism outperforms existing methods in the accuracy and discriminating speed under mixed malicious attacks.